Artificial Intelligence Chapter 12 Definition Artificial Intelligence AI

Artificial Intelligence Chapter 12

Definition: • Artificial Intelligence (AI): – “The activity of providing such machines as computers the ability to display behavior that would be regarded as intelligent if it were observed in humans. ”

AI ARTIFICIAL INTELLIGENCE (AI) SYSTEMS: (Laudon & Laudon Definition) AI: COMPUTER-BASED SYSTEMS WITH ABILITIES TO LEARN LANGUAGE, ACCOMPLISH TASKS, USE PERCEPTUAL APPARATUS, EMULATE HUMAN EXPERTISE & DECISION MAKING *

History of AI

1950 • Turing Test – "Can machines think? " • Loebner Prize – $100, 000 Grand Prize – Not yet awarded

1950: • Alan Turing proposes the “Turing Test” for computers • Can a computer pass for a human?

1952: • UNIVAC correctly predicts Dwight Eisenhower’s election with only 7% of votes reported •

1956: “Artificial Intelligence” • John Mc. Carthy coins the term in 1956 as theme of a conference held at Dartmouth College.

“Artificial Intelligence” • Dartmouth, 1956 • 25 -year Prediction (1981): – Prediction: in 25 years (1981) (would be before George Orwell’s 1984) – Intelligent machines would be able to do all the physical and intellectual work for human beings. – Leaving people to devote all their time to recreational activities.

1958: • John Mc. Carthy: • If we work really hard, we’ll have an intelligent system in from four to four hundred years.

1958: • Herbert Simon: • Said that a program would be chess champion in ten years (by 1968).

Deep Blue • 1997 IBM’s computer “Deep Blue” defeats world chess champion, Gary Kasparov. • First time a computer had defeated a top-ranked chess player • Not Undisputed

Major Areas of AI: • • Expert Systems Neural Networks Perceptive systems Robotics

AI EXPERT SYSTEMS KNOWLEDGE - INTENSIVE CAPTURES HUMAN EXPERTISE IN LIMITED DOMAINS OF KNOWLEDGE *

Development of Expert Systems • What is Expertise? – Skill and knowledge whose input into a process results in performance high above the norm.

First-to-100 -game • Rules: – 2 Players alternate by adding a number to the total. – Numbers must be within 1 -10. – First player to reach 100 wins

Following a Set of Rules / Pattern Recognition • The game can easily be won by anyone who recognizes the pattern… • You must be the first to 89 in order to be the first to 100…

Development of Expert Systems ® Components Systems of Expert ® The interface or dialog ® The knowledge base ® The interface engine

Development of Expert Systems Components of an expert system; numbers indicate the order of the processes

Expert Systems • The Benefits – Longevity – Cost savings – Availability – Replicable

Contribution of Expert Systems • Areas where ESs can help in business – – – – – Planning Decision making Monitoring Diagnosis Training Incidental learning Replication of expertise Timely response Consistent solutions

Contribution of Expert Systems Major reasons for using expert systems

Expert Systems in Action • Business areas using ESs – – – Telephone network maintenance Credit evaluation Tax planning Detection of insider securities trading Mineral exploration – Legal Advice/ Medical Advice – Visa & M/C: 2 purchases + 1 Gas out of town: call for verification

Knowledge Representation Methods • Factors Justifying the Acquisition of Expert Systems

AI • • EXPERT SYSTEMS LIMITATIONS: Often reduced to problems of classification Can be large, lengthy, expensive Maintaining knowledge base critical Many managers unwilling to trust such systems *

Limitations of Expert Systems • Three limitations of ESs – Can handle only narrow domains – Do not possess common sense – Have a limited ability to learn

Bobs Cars • Simple A. I. Application based on weights +/ - w/ each choice you make • http: //www. src-net. com/Bobs. Cars/fbdeal. htm

AI: Neural Networks

Neural Networks • Biologically inspired flexible statistical models. • Function approximations – Offers not only point estimates but also converges on the derivatives of the unknown functions

Neural Networks • A mathematical model of the human brain that simulates the way that neurons interact to process data and learn from experience.

Human Neurons • Dendrites (input) • Soma (processor) • Axons (output)

Biological Neural Network • Patterns of electrical impulses from cell to cell form memory.

From Biological to Artificial Neural Networks Neural nets simulate the association and inference that take place in a network of neurons in the human brain. Instead of a network of neurons, a network of nodes is developed.

Artificial Neuron • Y is the result of the weighted input signals • Any non-linear function can be used, the Sigmoid function is the most popular

Multi-Layered Artificial Neural Network (A. N. N. ) • All possible interactions are considered • All relationships are considered non-linear • High inter-correlation is not a problem

Specific Examples of A. N. N. • • • Bankruptcy Prediction Forecasting Stock Prices Direct Marketing Mail Prediction Credit Scoring Real Estate Appraisal Finding Gold (testing soil samples) Thoroughbred Horse Racing: 17 wins in 22 races Weather Forecasting Beer Testing Credit Card Fraud Detection • Mars-Rock testing

Neural Network Simulator for Character Recognition http: //diwww. epfl. ch/mantra/tutorial/english/ocr/html/index. html

AI: Ethical and Societal Issues

Ethical and Societal Issues Too Sophisticated Technology • Increasing dependence on machine intelligence raises legal and ethical issues. – Who is legally responsible for advice provided by a program? – Is expert judgment needed to interpret program output? – Does machine expertise replace or complement the ‘real thing’? – How do we know if the experts behind expert systems are expert at all?

Ethical and Societal Issues Too Sophisticated Technology • Malfunctions of an ES can be caused by anyone involved in the development. – Experts who contribute knowledge – Knowledge engineer who builds the system – Professional who uses the ES – The person who is affected by the decision

British Telecom • “Soul Catcher” • Implant a microchip in the human brain

Questions?

Needed Links:

Bobs Cars • Simple A. I. Application based on weights +/ - w/ each choice you make • http: //www. src-net. com/Bobs. Cars/fbdeal. htm

Neural Network Simulator for Character Recognition http: //diwww. epfl. ch/mantra/tutorial/english/ocr/html/index. html